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High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel

Published online by Cambridge University Press:  14 March 2019

Brian L. DeCost Open the ORCID record for Brian L. DeCost [Opens in a new window] ,
Bo Lei Open the ORCID record for Bo Lei [Opens in a new window] ,
Toby Francis Open the ORCID record for Toby Francis [Opens in a new window]  and
Elizabeth A. Holm Open the ORCID record for Elizabeth A. Holm [Opens in a new window]
Brian L. DeCost*
Affiliation:
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg MD, 20899, USA
Bo Lei
Affiliation:
Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA
Toby Francis
Affiliation:
Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA
Elizabeth A. Holm
Affiliation:
Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA
*
*Author for correspondence: Brian L. DeCost, E-mail: brian.decost@nist.gov
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Abstract

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.

Keywords

deep learningmicrostructuresegmentationsteel
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 1 , February 2019 , pp. 21 - 29
Copyright
Copyright © Microscopy Society of America 2019 

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